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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

**Natural language processing (NLP)** is located at the intersection of computational linguistics and machine learning. The main goal in this dynamic field is to extract information and insights from natural languages, meaning those that are spoken by humans in their everyday lives. NLP comprises a wide variety of methods and techniques, including topic modeling, sentiment analysis, machine translation, document summarization, and speech-to-text conversion.

In this chapter, we’ll focus on **topic modeling** because it’s supported by the NLP module of the PyCaret library. We can use this technique to discover **topics**,  the hidden structures that let us semantically group a collection of documents known as the corpus.


Learn how to import necessary libraries and datasets for natural language processing with PyCaret.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Natural Language Processing with PyCaret